文章基本信息

标题：Virtual Screening of EGFR Tyrosine Kinase Inhibitors Associated with Non-Small Cell Lung Cancer from Phytochemical Data Set
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作者：Priyanka Maiti ; Mahesha Nand ; Madhulata Kumari 等
期刊名称：Journal of Emerging Trends in Computing and Information Sciences
电子版ISSN：2079-8407
出版年度：2016
卷号：7
期号：5
页码：229-236
出版社：ARPN Publishers
摘要：Epidermal growth factor receptor (EGFR) is an important target to develop inhibitors against non-small cell lung cancers (NSCLC). Inhibition of EGFR by targeting the tyrosine kinase domain is a worldwide accepted treatment of NSCLC. The synthetic therapeutic drugs currently used in treatment of NSCLC cause serious side effects and costly too. In other hand, numerous plant derived compounds show excellent potential as cancer growth inhibitors with low side effect and cost effective too. The present study aims to identify potential plant derived inhibitors against EGFR tyrosine kinase ATP binding domain using computational approaches. 60 phytochemicals from five different plants namely Acorus calamus, Asparagus racemosus, Moringa oleifera, Withania somnifera and Rhododendron Arboreum were used to screen for their inhibitory property against EGFR. The primary screening was done using machine learning model with a hybrid dataset of PubChem bioassay AID: 256664 and AID 624370. Further screening was carried out by docking using Autodock vina, followed by X-Score and CDRUG analysis. 20 phytochemicals showed active potential in machine learning model. Out of them 18 phytochemicals had more negative binding energy ranges from -4.4 to -8.8 kcal/mol against EGFR as compare with reference compound. Further 19 phytochemicals were screened by CDRUG which showed the high probability to be as anticancer agents. 16 phytochemicals out of 60 were screened by all these three techniques. Pharmacophore analysis was then performed to find out common pharmacophores by PharmaGist server. All the selected phytochemicals were further annotated for their drug likeness and toxicity profile by using FAF-Drug3 and admetSAR which resulted 12 phytochemicals with drug approval and good ADMET profiles. Finally, these compounds were evaluated for their cytotoxicity on cell lines by CLC-Pred. This yielded 9 hit phytochemicals as potent natural inhibitors of EGFR tyrosine kinase which could be used in future drug development against NSCLC.
关键词：NSCLC; EGFR inhibitors; virtual screening; machine learning model; docking; X-Score; CDRUG; PharmaGist; FAF-Drug3; admetSAR; CLC-Pred.